19th – 25th June 2021 (virtual).
CVPR is the premier annual computer vision event.
We’d like to congratulate the six members of our research staff who have been recognised as ‘Outstanding Reviewers’ at this year’s conference.
NAVER is a Spotlight sponsor of CVPR 2021
Workshop: Women in Computer Vision (WiCV).
Diane Larlus, NAVER LABS Europe, invited speaker: “Lifelong representation learning”
Workshop: International Challenge on Activity Recognition (ActivityNet)
Yannis Kalantidis, NAVER LABS Europe, co-organising the ActivityNet-Entities challenge
NAVER LABS Europe is a sponsor of this challenge.
11:00-03:30 EST | 17:00-19:30 CEST | 00:00-02:30 (+1 day) KST | Paper Session 1
Rethinking channel dimensions for efficient model design*
Dongyoon Han, Sangdoo Yun, Byeongho Heo, YoungJoon Yoo
[pdf] [supp] [arXiv]
Exploiting spatial dimensions of latent in GAN for real-time image editing*
Hyunsu Kim, Yunjey Choi, Junho Kim, Sungjoo Yoo, Youngjung Uh
[pdf] [supp] [arXiv]
Looking into your speech: learning cross-modal affinity for audio-visual speech separation*
Jiyoung Lee, Soo-Whan Chung, Sunok Kim, Hong-Goo Kang, Kwanghoon Sohn
[pdf] [supp] [arXiv]
22:00-00:30 EST | 04:00-06:30 (+1 day) CEST | 11:00-13:30 (+1 day) KST | Paper Session 2
Re-labeling ImageNet: from single to multi-labels, from global to localized labels*
Sangdoo Yun, Seong Joon Oh, Byeongho Heo, Dongyoon Han, Junsuk Choe, Sanghyuk Chun
[pdf] [supp] [arXiv]
06:00-08:30 EST | 12:00-14:30 CEST | 19:00-21:30 KST | Paper Session 3
Large-scale localization datasets in crowded indoor spaces
Donghwan Lee, Soohyun Ryu, Suyong Yeon, Yonghan Lee, Deokhwa Kim, Cheolho Han, Yohann Cabon, Philippe Weinzaepfel, Nicolas Guérin, Gabriela Csurka, Martin Humenberger
[pdf] [supp] [arXiv]
11:00-13:30 EST | 17:00-19:30 CEST | 00:00-02:30 (+1) KST | Paper Session 4
Continual adaptation of visual representations via domain randomization and meta-learning. Oral.
Riccardo Volpi, Diane Larlus, Grégory Rogez
[pdf] [supp] [arXiv]
06:00-08:30 EST | 12:00-14:30 CEST | 19:00-21:30 KST | Paper Session 6
Probabilistic embeddings for cross-modal retrieval
Sanghyuk Chun, Seong Joon Oh, Rafael Sampaio de Rezende, Yannis Kalantidis, Diane Larlus
[pdf] [supp] [arXiv]
Rainbow memory: continual learning with a memory of diverse samples*
Jihwan Bang, Heesu Kim, Youngjoon Yoo, Jung-Woo Ha, Jonghyun Choi (GIST)
[pdf] [supp] [arXiv]
Workshop: Continual Learning
Learning in a changing environment: memory strategies for streaming learning under distributional shifts. [pdf] Findings
Riccardo Volpi, Cesar de Souza, Yannis Kalantidis, Diane Larlus, Grégory Rogez
Workshop: Omnidirectional Computer Vision in Research and Industry
Detecting low-rank regions in omnidirectional images [pdf]
Zoltan Kato, Gabor Nagy, Martin Humenberger, Gabriela Csurka Khedari
Workshop: AI for Content Creation (AICC)
Exploiting spatial dimensions of latent in GAN for real-time image editing* [arXiv] [Webpage]
Hyunsu Kim, Yunjey Choi, Junho Kim, Sungjoo Yoo, Youngjung Uh
Few-shot font generation with localized style representations and factorization* [arXiv] [Webpage]
Song Park (Yonsei University), Sanghyuk Chun (NAVER AI LAB), Junbum Cha (Clova AI Research, NAVER Corp.), Bado Lee (Clova AI Research, NAVER Corp.), Hyunjung Shim (Yonsei University)
To make robots autonomous in real-world everyday spaces, they should be able to learn from their interactions within these spaces, how to best execute tasks specified by non-expert users in a safe and reliable way. To do so requires sequential decision-making skills that combine machine learning, adaptive planning and control in uncertain environments as well as solving hard combinatorial optimization problems. Our research combines expertise in reinforcement learning, computer vision, robotic control, sim2real transfer, large multimodal foundation models and neural combinatorial optimization to build AI-based architectures and algorithms to improve robot autonomy and robustness when completing everyday complex tasks in constantly changing environments. More details on our research can be found in the Explore section below.
For a robot to be useful it must be able to represent its knowledge of the world, share what it learns and interact with other agents, in particular humans. Our research combines expertise in human-robot interaction, natural language processing, speech, information retrieval, data management and low code/no code programming to build AI components that will help next-generation robots perform complex real-world tasks. These components will help robots interact safely with humans and their physical environment, other robots and systems, represent and update their world knowledge and share it with the rest of the fleet. More details on our research can be found in the Explore section below.
Visual perception is a necessary part of any intelligent system that is meant to interact with the world. Robots need to perceive the structure, the objects, and people in their environment to better understand the world and perform the tasks they are assigned. Our research combines expertise in visual representation learning, self-supervised learning and human behaviour understanding to build AI components that help robots understand and navigate in their 3D environment, detect and interact with surrounding objects and people and continuously adapt themselves when deployed in new environments. More details on our research can be found in the Explore section below.
Details on the gender equality index score 2024 (related to year 2023) for NAVER France of 87/100.
1. Difference in female/male salary: 34/40 points
2. Difference in salary increases female/male: 35/35 points
3. Salary increases upon return from maternity leave: Non calculable
4. Number of employees in under-represented gender in 10 highest salaries: 5/10 points
The NAVER France targets set in 2022 (Indicator n°1: +2 points in 2024 and Indicator n°4: +5 points in 2025) have been achieved.
——————-
Index NAVER France de l’égalité professionnelle entre les femmes et les hommes pour l’année 2024 au titre des données 2023 : 87/100
Détail des indicateurs :
1. Les écarts de salaire entre les femmes et les hommes: 34 sur 40 points
2. Les écarts des augmentations individuelles entre les femmes et les hommes : 35 sur 35 points
3. Toutes les salariées augmentées revenant de congé maternité : Incalculable
4. Le nombre de salarié du sexe sous-représenté parmi les 10 plus hautes rémunérations : 5 sur 10 points
Les objectifs de progression de l’Index définis en 2022 (Indicateur n°1 : +2 points en 2024 et Indicateur n°4 : +5 points en 2025) ont été atteints.
To make robots autonomous in real-world everyday spaces, they should be able to learn from their interactions within these spaces, how to best execute tasks specified by non-expert users in a safe and reliable way. To do so requires sequential decision-making skills that combine machine learning, adaptive planning and control in uncertain environments as well as solving hard combinatorial optimisation problems. Our research combines expertise in reinforcement learning, computer vision, robotic control, sim2real transfer, large multimodal foundation models and neural combinatorial optimisation to build AI-based architectures and algorithms to improve robot autonomy and robustness when completing everyday complex tasks in constantly changing environments.
The research we conduct on expressive visual representations is applicable to visual search, object detection, image classification and the automatic extraction of 3D human poses and shapes that can be used for human behavior understanding and prediction, human-robot interaction or even avatar animation. We also extract 3D information from images that can be used for intelligent robot navigation, augmented reality and the 3D reconstruction of objects, buildings or even entire cities.
Our work covers the spectrum from unsupervised to supervised approaches, and from very deep architectures to very compact ones. We’re excited about the promise of big data to bring big performance gains to our algorithms but also passionate about the challenge of working in data-scarce and low-power scenarios.
Furthermore, we believe that a modern computer vision system needs to be able to continuously adapt itself to its environment and to improve itself via lifelong learning. Our driving goal is to use our research to deliver embodied intelligence to our users in robotics, autonomous driving, via phone cameras and any other visual means to reach people wherever they may be.
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